The large Doppler offset that exists in high dynamic environments poses a serious impediment to the acquisition of direct sequence spread spectrum (DSSS) signals. To ensure acceptable detection probabilities, the frequency space has to be finely divided, which leads to complicated acquisition structures and excessively long acquisition time at low SNR. A local frequency folding (LFF) method designed for combined application with established techniques dedicated to PN-code synchronization is proposed in this paper. Through modulating local PN-code block with a fixed waveform obtained by folding all frequency cells together, we eliminate the need for frequency search and ease the workload of acquisition. We also analyze the performance of LFF and find that the detection performance degradation from folding can be compensated by FFT-based coherent integration. The study is complemented with numerical simulations showing that the proposed method has advantages over unfolding methods with respect to detection probability and mean acquisition time, and the advantage becomes obvious but limited if the folded number gets larger.

There have been many researches about construction and application of ontology in reality, notably the usage of ontology to support requirements engineering. The effect of ontology-based requirements engineering depends on quality of ontology. With the increasing size of ontology, it is difficult to verify the correctness of information stored in ontology. This paper will propose a method of using rules for verification the correctness of requirements ontology. We provide a rule description language to specify properties that requirements ontology should satisfy. Then, by checking whether the rules are consistent with requirements ontology, we verify the correctness of the ontology. We have developed a verification tool to support the method and evaluated the tool through experiments.

The stochastic behavior of inter-core crosstalk in multi-core fiber is discussed based on a theoretical model validated by measurements, and the effect of the crosstalk on the Q-factor in transmission systems, using multi-core fiber is investigated theoretically. The measurements show that the crosstalk rapidly changes with wavelength, and gradually changes with time, in obedience to the Gaussian distribution in I-Q planes. Therefore, the behavior of the crosstalk as a noise may depend on the bandwidth of the signal light. If the bandwidth is adequately broad, the crosstalk may behave as a virtual additive white Gaussian noise on I-Q planes, and the Q-penalty at the Q-factor of 9.8dB is less than 1dB when the statistical mean of the crosstalk from other cores is less than -16.7dB for PDM-QPSK, -23.7dB for PDM-16QAM, and -29.9dB for PDM-64QAM. If the bandwidth is adequately narrow, the crosstalk may behave as virtually static coupling that changes very gradually with time and heavily depends on the wavelength. To cope with a static crosstalk much higher than its statistical mean, a margin of several decibels from the mean crosstalk may be necessary for suppressing Q-penalty in the case of adequately narrow bandwidth.

The complicated indoor environment such as obstacles causes the non-line of sight (NLOS) environment. In this paper, we propose a voting matrix based residual weighting (VM-Rwgh) algorithm to mitigate NLOS errors in indoor localization system. The voting matrix is employed to provide initial localization results. The residual weighting is used to improve the localization accuracy. The VM-Rwgh algorithm can overcome the effects of NLOS errors, even when more than half of the measurements contain NLOS errors. Simulation results show that the VM-Rwgh algorithm provides higher location accuracy with relatively lower computational complexity in comparison with other methods.

Developers often face difficulties while using APIs. API usage patterns can aid them in using APIs efficiently, which are extracted from source code stored in software repositories. Previous approaches have mined repositories to extract API usage patterns by simply applying data mining techniques to the collection of method invocations of API objects. In these approaches, respective functional roles of invoked methods within API objects are ignored. The functional role represents what type of purpose each method actually achieves, and a method has a specific predefined order of invocation in accordance with its role. Therefore, the simple application of conventional mining techniques fails to produce API usage patterns that are helpful for code completion. This paper proposes an improved approach that extracts API usage patterns at a higher abstraction level rather than directly mining the actual method invocations. It embraces a multilevel sequential mining technique and uses categorization of method invocations based on their functional roles. We have implemented a mining tool and an extended Eclipse's code completion facility with extracted API usage patterns. Evaluation results of this tool show that our approach improves existing code completion.

In this paper, the diversity multiplexing tradeoff (DMT) analysis of the non-coherent block-fading multiple antenna channel which uses a training-based channel estimation scheme at asymptotically high signal-to-noise ratios (SNRs) is extended to finite SNRs. This extension is performed for a single input multiple output (SIMO) maximal ratio combining (MRC) scheme. This analysis at finite SNRs is more useful because in practice, the training schemes operate at finite SNRs and their impact on DMT is more relevant at such SNRs. We show the non-applicability of the asymptotically high SNR relation, given by Zheng, to finite SNRs. We also show the equivalence of two existing training-based channel estimation schemes for any SIMO system, and using one of these, we compute the achievable finite-SNR DMT of the non-coherent SIMO-MRC scheme for two modes of the training scheme. We analyze the achievable finite-SNR DMT for different durations of training, modes of the training scheme, and SNRs. We show that the impact of the mode of the training scheme on finite-SNR DMT decreases as SNR increases. We also show that at asymptotically high SNRs, the achievable DMT in both modes of the SIMO-MRC scheme is equal to that of the non-coherent SIMO channel, as derived by Zheng.

This paper describes a method for the efficient computation of the total autocorrelation for large multiple-output Boolean functions over a Shared Binary Decision Diagram (SBDD). The existing methods for computing the total autocorrelation over decision diagrams are restricted to single output functions and in the case of multiple-output functions require repeating the procedure k times where k is the number of outputs. The proposed method permits to perform the computation in a single traversal of SBDD. In that order, compared to standard BDD packages, we modified the way of traversing sub-diagrams in SBDD and introduced an additional memory function kept in the hash table for storing results of the computation of the autocorrelation between two subdiagrams in the SBDD. Due to that, the total amount of computations is reduced which makes the method feasible in practical applications. Experimental results over standard benchmarks confirm the efficiency of the method.

We proposes a new adaptive spectral masking method of algebraic vector quantization (AVQ) for non-sparse signals in the modified discreet cosine transform (MDCT) domain. This paper also proposes switching the adaptive spectral masking on and off depending on whether or not the target signal is non-sparse. The switching decision is based on the results of MDCT-domain sparseness analysis. When the target signal is categorized as non-sparse, the masking level of the target MDCT coefficients is adaptively controlled using spectral envelope information. The performance of the proposed method, as a part of ITU-T G.711.1 Annex D, is evaluated in comparison with conventional AVQ. Subjective listening test results showed that the proposed method improves sound quality by more than 0.1 points on a five-point scale on average for speech, music, and mixed content, which indicates significant improvement.

We have succeeded in highly selective growth and positioning of Si- and SiGe-quantum-dots (QDs) on SiO2 patterns by controlling the reactive area, whose surface is terminated with OH bonds for Si nucleation in low-pressure chemical vapor deposition (LPCVD). The selective growth of QDs on thermally grown SiO2 line-patterns was demonstrated in LPCVD of SiH4 and GeH4 just after Si nucleation by controlling the early stages of Si2H6-LPCVD, which indicates effectively enhanced initial nucleation on OH-terminated SiO2 surface and suppression of the nucleation and growth of dots on as-grown SiO2 surface during Si2H6-LPCVD prior to SiH4-LPCVD.

A low VDD current mirror with deep sub-micron vertical MOSFETs is presented. The keys are new bias circuits to reduce both the minimum VDD for the operation and the sensitivity of the output current on VDD. In the simulation, our circuits reduce the minimum VDD by about 17% and the VDD sensitivity by one order both from those of the conventional. In the simulation with 90nm φ vertical MOSFET approximate models, our circuit shows about 4MΩ output resistance at 1.2-V VDD with the small temperature dependence, which is about six times as large as that with planar MOSFETs.

The delay time component (τs) of an InGaAs MOSFET caused by dynamic source resistance is discussed. On the basis of the relationship between the current density (J) and the dynamic source resistance (rs), the value of rs is proportional to 1/J with some offset at low current densities, whereas the offset becomes smaller in a region of high current density. The value of τs depends on the current in a way similar to rs. Because the offset in the high-current-density region is proportional to the square root of the effective mass, an InGaAs MOSFET with a small mass has a shorter rs than a Si MOSFET.

The success of any project can be affected by requirements changes. Requirements elicitation is a series of activities of adding, deleting, and modifying requirements. We refer to the completion of requirements elicitation of a software component as requirements maturation. When the requirements of each component have reached the 100% maturation point, no requirement will come to the component. This does not mean that a requirements analyst (RA) will reject the addition of requirements, but simply, that the additional requirements will not come to the project. Our motivation is to provide measurements by which an RA can estimate one of the maturation periods: the early, middle, or late period of the project. We will proceed by introducing the requirements maturation efficiency (RME). The RME of the requirements represents how quickly the requirements of a component reach 100% maturation. Then, we will estimate the requirements maturation period for every component by applying the RME. We assume that the RME is derived from its accessibility from an RA to the requirements source and the stability of the requirements. We model accessibility as the number of information flows from the source of the requirements to the RA, and further, model stability with the requirements maturation index (RMI). According to the multiple regression analysis of a case, we are able to get an equation on RME derived from these two factors with a significant level of 5%. We evaluated the result by comparing it to another case, and then discuss the effectiveness of the measurements.

To improve metal oxide semiconductor field effect transistors (MOSFET) performance, flat interface between gate insulator and silicon (Si) should be realized. In this paper, the influence of Si surface roughness on electrical characteristics of MOSFET with hafnium oxynitride (HfON) gate insulator formed by electron cyclotron resonance (ECR) plasma sputtering was investigated for the first time. The surface roughness of Si substrate was reduced by Ar/4.9%H2 annealing utilizing conventional rapid thermal annealing (RTA) system. The obtained root-mean-square (RMS) roughness was 0.07nm (without annealed: 0.18nm). The HfON was formed by 2nm-thick HfN deposition followed by the Ar/O2 plasma oxidation. The electrical properties of HfON gate insulator were improved by reducing Si surface roughness. It was found that the current drivability of fabricated nMOSFETs was remarkably increased by reducing Si surface roughness. Furthermore, the reduction of Si surface roughness also leads to decrease of the 1/f noise.

Numerous studies have been focusing on the improvement of bag of features (BOF), histogram of oriented gradient (HOG) and scale invariant feature transform (SIFT). However, few works have attempted to learn the connection between them even though the latter two are widely used as local feature descriptor for the former one. Motivated by the resemblance between BOF and HOG/SIFT in the descriptor construction, we improve the performance of HOG/SIFT by a) interpreting HOG/SIFT as a variant of BOF in descriptor construction, and then b) introducing recently proposed approaches of BOF such as locality preservation, data-driven vocabulary, and spatial information preservation into the descriptor construction of HOG/SIFT, which yields the BOF-driven HOG/SIFT. Experimental results show that the BOF-driven HOG/SIFT outperform the original ones in pedestrian detection (for HOG), scene matching and image classification (for SIFT). Our proposed BOF-driven HOG/SIFT can be easily applied as replacements of the original HOG/SIFT in current systems since they are generalized versions of the original ones.

The authors have evaluated a method of expanding the bit depth of image signals called SGRAD, which requires fewer calculations, while degrading the sharpness of images less. Where noise is superimposed on image signals, the conventional method for obtaining high bit depth sometimes incorrectly detects the contours of images, making it unable to sufficiently correct the gradation. Requiring many line memories is also an issue with the conventional method when applying the process to vertical gradation. As a solution to this particular issue, SGRAD improves the method of detecting contours with transiting gradation to effectively correct the gradation of image signals which noise is superimposed on. In addition, the use of a prediction algorithm for detecting gradation reduces the scale of the circuit with less correction of the vertical gradation.

This letter presents an automatic 2D-to-3D conversion method using a structure from motion (SfM) process for multi-object scenes. The foreground and background regions may have different depth values in an image. First, we detect the foreground objects and the background by using a depth histogram. Then, the proposed method creates the virtual image by projecting each region with its computed projective matrix. Experimental results compared to previous research show that the proposed method provides realistic stereoscopic images.

Turbo mode, which accelerates many applications without major change of existing systems, is widely used in commercial processors. Since time duration or powerfulness of turbo mode depends on peak temperature of a processor chip, reducing the peak temperature can reinforce turbo mode. This paper presents that adding small amount of hardware allows microprocessors to reduce the peak temperature drastically and then to reinforce turbo mode successfully. Our approach is to find out a few small units that become heat sources in a processor and to appropriately duplicate them for reduction of their power density. By duplicating the limited units and using the copies evenly, the processor can show significant performance improvement while achieving area-efficiency. The experimental result shows that the proposed method achieves up to 14.5% of performance improvement in exchange for 2.8% of area increase.

In this paper, a one-class Naïve Bayesian classifier (One-NB) for detecting toll frauds in a VoIP service is proposed. Since toll frauds occur irregularly and their patterns are too diverse to be generalized as one class, conventional binary-class classification is not effective for toll fraud detection. In addition, conventional novelty detection algorithms have struggled with optimizing their parameters to achieve a stable detection performance. In order to resolve the above limitations, the original Naïve Bayesian classifier is modified to handle the novelty detection problem. In addition, a genetic algorithm (GA) is employed to increase efficiency by selecting significant variables. In order to verify the performance of One-NB, comparative experiments using five well-known novelty detectors and three binary classifiers are conducted over real call data records (CDRs) provided by a Korean VoIP service company. The experimental results show that One-NB detects toll frauds more accurately than other novelty detectors and binary classifiers when the toll frauds rates are relatively low. In addition, The performance of One-NB is found to be more stable than the benchmark methods since no parameter optimization is required for One-NB.

A service-oriented architecture builds the entire system using a combination of independent software components. Such an architecture can be applied to a wide variety of computer systems. The problem of synthesizing service implementation models from choreography representing the overall specifications of service interaction is known as the choreography realization problem. In automatic synthesis, software models should be simple enough to be easily understood by software engineers. In this paper, we discuss a semi-formal method for synthesizing hierarchical state machine models for the choreography realization problem. The proposed method is evaluated using metrics for intelligibility.

This letter presents a new entropy measure for electroencephalograms (EEGs), which reflects the underlying dynamics of EEG over multiple time scales. The motivation behind this study is that neurological signals such as EEG possess distinct dynamics over different spectral modes. To deal with the nonlinear and nonstationary nature of EEG, the recently developed empirical mode decomposition (EMD) is incorporated, allowing an EEG to be decomposed into its inherent spectral components, referred to as intrinsic mode functions (IMFs). By calculating Shannon entropy of IMFs in a time-dependent manner and summing them over adaptive multiple scales, the result is an adaptive subscale entropy measure of EEG. Simulation and experimental results show that the proposed entropy properly reveals the dynamical changes over multiple scales.